Image interpolation is a popular technique for manipulating and processing images. One of the widely-used applications of the image interpolation techniques is to provide enhanced visual effects after a digital image is scaled up or scaled down. Consequently, the efficacy of displaying or printing the image is enhanced. In recent years, the increasing popularity of consumer multimedia products has made imaging devices or display devices (e.g. flat display devices or digital cameras) ubiquitous. As a result, the image interpolation technique is becoming increasingly important.
For example, since the size and the resolution of the flat display devices are gradually increased and the demands on full-screen display modes are increased, the image interpolation technique becomes a necessary procedure. As known, if the resolution of the image inputted into the flat display device is too low and the size of the flat display device is too large, the image interpolation technique becomes more important. In this situation, if the image interpolation method is not done, the full-screen image is blurry during image visualization. The blurry image is detrimental to observation. Moreover, the image interpolation technique is also helpful for the operations of other image input devices. For example, the image interpolation technique is helpful for increasing the resolution of scanners or the zooming functions of digital cameras.
FIG. 1 schematically illustrates some image interpolation results obtained by some conventional image interpolation methods. In FIG. 1A, an original image is shown. After a high resolution original image is sampled as a low resolution target image, the image interpolation result of FIG. 1B is obtained by a nearest neighbor interpolation method. After a high resolution original image is sampled as a low resolution target image, the image interpolation result of FIG. 1C is obtained by a bi-linear interpolation method. After a high resolution original image is sampled as a low resolution target image, the image interpolation result of FIG. 1D is obtained by a bi-cubic interpolation method.
From FIG. 1, it is found that the conventional image interpolation methods have some drawbacks. For example, the features of the interpolated image usually have obvious aliasing and blocking effects. In addition, the features of the interpolated image (e.g. the edge features and the line features of the image) produce a blurry effect. Consequently, the interpolated image is suffered from a defocus problem during image visualization.
For solving the above drawbacks, an image interpolation method based on a probabilistic neural network (PNN) has been disclosed. FIG. 2 schematically illustrates an image interpolation result obtained by a conventional image interpolation method based on a probabilistic neural network (PNN). By comparing the image interpolation results of FIGS. 1 and 2, it is found that the PNN method can achieve the better image interpolation result. Generally, the probabilistic neural network uses a Gaussian function as a kernel. Since the Gaussian kernel function has isotropic characteristics and low pass filter characteristics, the sharpness characteristics in the edge region with drastic gray level variation are not very obvious. Consequently, the edge features and the line features of the interpolated image are slightly suffered from a defocus problem during image visualization.
For solving the above drawbacks, an image interpolation method based on an adaptive probabilistic neural network (APNN) has been disclosed. FIG. 3 schematically illustrates an image interpolation result obtained by a conventional image interpolation method based on an adaptive probabilistic neural network (APNN). By comparing the image interpolation results of FIGS. 2 and 3, it is found that the APNN method can achieve the better sharpness enhancement.
Since the APNN method calculates the edge direction angles, the Gaussian function has anisotropic characteristics. Under this circumstance, the interpolated values of the interpolation points are closer to the edge characteristics of the original image. However, the Gaussian function parameters corresponding to plural neighboring reference points of the interpolation point are obtained along an identical edge direction angle (e.g. along the edge direction angle of the interpolation point). Under this circumstance, the interpolated values of the interpolation points still fail to approach the edge characteristics of the original image. In other words, the interpolated image of FIG. 3 is still slightly blurry.
Therefore, there is a need of providing an improved image interpolation method.